Advertisement

Data Dictionary Extraction for Robust Emergency Detection

  • Emanuele CipollaEmail author
  • Filippo Vella
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 55)

Abstract

In this work we aim at generating association rules starting from meteorological measurements from a set of heterogeneous sensors displaced in a region. To create rules starting from the statistical distribution of the data we adaptively extract dictionaries of values. We use these dictionaries to reduce the data dimensionality and represent the values in a symbolic form. This representation is driven by the set of values in the training set and is suitable for the extraction of rules with traditional methods. Furthermore we adopt the boosting technique to build strong classifiers out of simpler association rules: their use shows promising results with respect to their accuracy a sensible increase in performance.

Keywords

Sensor Network Association Rule Vector Quantization Association Rule Mining Weak Classifier 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work has been partially funded by project SIGMA PONOI_00683 Sistema Integrato di sensori in ambiente cloud per la Gestione Multirischio Avanzata PON MIUR R&C 2007–2013.

References

  1. 1.
    Cipolla, E., Maniscalco, U., Rizzo, R., Stabile, D., Vella, F.: Analysis and visualization of meteorological emergencies. J. Ambient Intell. Hum. Comput. 1–12 (2016)Google Scholar
  2. 2.
    Li, H., Wang, Y., Zhang, D., Zhang, M., Chang, E.Y.: Pfp: parallel fp-growth for query recommendation. In: Proceedings of the 2008 ACM Conference on Recommender Systems, ser. RecSys ’08, pp. 107–114. ACM, New York, NY, USA (2008)Google Scholar
  3. 3.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. SIGMOD Rec. 29(2), 1–12 (2000)CrossRefGoogle Scholar
  4. 4.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)CrossRefGoogle Scholar
  6. 6.
    Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  7. 7.
    Yoon, Y., Lee, G.G.: Text categorization based on boosting association rules. In: IEEE International Conference on Semantic Computing, vol. 2008, pp. 136–143 (2008)Google Scholar
  8. 8.
    Lloyd, S.: Least squares quantization in pcm. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Trans. Commun. 28, 84–95 (1980)CrossRefGoogle Scholar
  10. 10.
    Cipolla, E., Vella, F., Boosting of association rules for robust emergency detection. In: 11th International Conference on Signal-Image Technology & Internet-Based Systems, SITIS 2015, pp. 185–191. Bangkok, Thailand, 23-27 Nov 2015Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute for High Performance Computing and Networking - ICAR, National Research Council of ItalyPalermoItaly

Personalised recommendations